18 citations · 29 across the 28 of their papers we have counts for
10 papers · 1 filter
Next Token Prediction Towards Multimodal Intelligence: A Comprehensive Survey
Liang Chen, Zekun Wang, Shuhuai Ren +24
Building on the foundations of language modeling in natural language processing, Next Token Prediction (NTP) has evolved into a versatile training objective for machine learning ta…
Looking Beyond Text: Reducing Language bias in Large Vision-Language Models via Multimodal Dual-Attention and Soft-Image Guidance
Haozhe Zhao, Shuzheng Si, Liang Chen +4
Large vision-language models (LVLMs) have achieved impressive results in various vision-language tasks. However, despite showing promising performance, LVLMs suffer from hallucinat…
A Spark of Vision-Language Intelligence: 2-Dimensional Autoregressive Transformer for Efficient Finegrained Image Generation
Liang Chen, Sinan Tan, Zefan Cai +7
This work tackles the information loss bottleneck of vector-quantization (VQ) autoregressive image generation by introducing a novel model architecture called the 2-Dimensional Aut…
GATEAU: Selecting Influential Samples for Long Context Alignment
Shuzheng Si, Haozhe Zhao, Gang Chen +7
Aligning large language models to handle instructions with extremely long contexts has yet to be fully investigated. Previous studies have attempted to scale up the available data…
Rethinking Semantic Parsing for Large Language Models: Enhancing LLM Performance with Semantic Hints
Kaikai An, Shuzheng Si, Helan Hu +4
Semantic Parsing aims to capture the meaning of a sentence and convert it into a logical, structured form. Previous studies show that semantic parsing enhances the performance of s…
UltraEdit: Instruction-based Fine-Grained Image Editing at Scale
Haozhe Zhao, Xiaojian Ma, Liang Chen +7
This paper presents UltraEdit, a large-scale (approximately 4 million editing samples), automatically generated dataset for instruction-based image editing. Our key idea is to addr…
MMEvalPro: Calibrating Multimodal Benchmarks Towards Trustworthy and Efficient Evaluation
Jinsheng Huang, Liang Chen, Taian Guo +13
Large Multimodal Models (LMMs) exhibit impressive cross-modal understanding and reasoning abilities, often assessed through multiple-choice questions (MCQs) that include an image,…
Mitigating Language-Level Performance Disparity in mPLMs via Teacher Language Selection and Cross-lingual Self-Distillation
Haozhe Zhao, Zefan Cai, Shuzheng Si +4
Large-scale multilingual Pretrained Language Models (mPLMs) yield impressive performance on cross-language tasks, yet significant performance disparities exist across different lan…
An Image is Worth 1/2 Tokens After Layer 2: Plug-and-Play Inference Acceleration for Large Vision-Language Models
Liang Chen, Haozhe Zhao, Tianyu Liu +4
In this study, we identify the inefficient attention phenomena in Large Vision-Language Models (LVLMs), notably within prominent models like LLaVA-1.5, QwenVL-Chat and Video-LLaVA.…
PCA-Bench: Evaluating Multimodal Large Language Models in Perception-Cognition-Action Chain
Liang Chen, Yichi Zhang, Shuhuai Ren +7
We present PCA-Bench, a multimodal decision-making benchmark for evaluating the integrated capabilities of Multimodal Large Language Models (MLLMs). Departing from previous benchma…